贝叶斯分析对磁性纳米颗粒表征的好处
The benefits of a Bayesian analysis for the characterization of magnetic nanoparticles
摘要：
磁性纳米粒子为各种生物医学应用提供了独特的潜力，但在商业使用之前，需要对其结构和磁性进行标准化表征。对于一个彻底的表征，传统的磁力测量和先进的散射技术相结合，已显示出巨大的潜力。在目前的工作中，我们的特点是高品质的氧化铁纳米颗粒的粉末样品，由直流磁力计和磁小角中子散射（SANS）包围着均匀的厚二氧化硅壳。为了从数据中检索颗粒参数，例如它们的尺寸分布和饱和磁化强度，我们应用单个数据集的标准模型拟合以及多条曲线的全局拟合，包括磁力测量和SANS测量的组合。我们表明，通过将标准最小二乘拟合与随后的贝叶斯方法相结合，可以很容易地提取模型参数及其互相关的概率分布，这使得直接的视觉反馈的拟合质量。这防止了在高度相关的参数的情况下数据的过拟合，并且使得贝叶斯方法成为磁性纳米颗粒样品的标准化数据分析的理想组成部分。
英文摘要：
Magnetic nanoparticles offer a unique potential for various biomedical applications, but prior to commercial usage a standardized characterization of their structural and magnetic properties is required. For a thorough characterization, the combination of conventional magnetometry and advanced scattering techniques has shown great potential. In the present work, we characterize a powder sample of high-quality iron oxide nanoparticles that are surrounded with a homogeneous thick silica shell by DC magnetometry and magnetic small-angle neutron scattering (SANS). To retrieve the particle parameters such as their size distribution and saturation magnetization from the data, we apply standard model fits of individual data sets as well as global fits of multiple curves, including a combination of the magnetometry and SANS measurements. We show that by combining a standard least-squares fit with a subsequent Bayesian approach for the data refinement, the probability distributions of the model parameters and their cross correlations can be readily extracted, which enables a direct visual feedback regarding the quality of the fit. This prevents an overfitting of data in case of highly correlated parameters and renders the Bayesian method as an ideal component for a standardized data analysis of magnetic nanoparticle samples.
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